High-resolution hyperspectral images of Council, Alaska 2019
Hyperspectral data are gaining popularity in remote sensing and signal processing communities because of the increased spectral information relative to multispectral data. Several airborne and spaceborne hyperspectral datasets are publicly available, facilitating the development of various applicati...
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NSF Arctic Data Center
2021
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ftdatacite:10.18739/a2416t10d 2023-05-15T17:57:49+02:00 High-resolution hyperspectral images of Council, Alaska 2019 Chi, Junhwa Kim, Jae-In Masjedi, Ali Flatt, John Evan Crawford, Melba M. Habib, Ayman F. Lee, Joohan Kim, Hyun-Cheol 2021 text/xml https://dx.doi.org/10.18739/a2416t10d https://arcticdata.io/catalog/view/doi:10.18739/A2416T10D en eng NSF Arctic Data Center Remote sensing Hyperspectral UAV Permafrost Council, Alaska dataset Dataset 2021 ftdatacite https://doi.org/10.18739/a2416t10d 2021-11-05T12:55:41Z Hyperspectral data are gaining popularity in remote sensing and signal processing communities because of the increased spectral information relative to multispectral data. Several airborne and spaceborne hyperspectral datasets are publicly available, facilitating the development of various applications and algorithms. However, hyperspectral data are usually limited by their narrow, highly correlated, and contiguous spectral bands in both processing and analysis. Moreover, the resolution of available hyperspectral datasets is not sufficiently high for the identification of small objects. Nevertheless, with the rapidly advancing technology, hyperspectral imaging systems can now be mounted on small aerial vehicles for detecting small objects at low altitude. To properly handle these high spectral and spatial resolution data, new or redesigned data processing or analysis pipelines must be developed. However, such datasets are currently not publicly available. Therefore, we provide two hyperspectral datasets from GNSS/INS-assisted co-aligned pushbroom hyperspectral scanners on a drone. The hyperspectral datasets consist of raw/post-processed hyperspectral data, raw/post-processed Global Navigation System Satellite/Inertial Measurement Unit (GNSS/IMU) data, and digital surface models, and were radiometrically and geometrically evaluated. These datasets are expected to aid the improvement of UAV-based hyperspectral data processing and analysis algorithms. Dataset permafrost Alaska DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
Remote sensing Hyperspectral UAV Permafrost Council, Alaska |
spellingShingle |
Remote sensing Hyperspectral UAV Permafrost Council, Alaska Chi, Junhwa Kim, Jae-In Masjedi, Ali Flatt, John Evan Crawford, Melba M. Habib, Ayman F. Lee, Joohan Kim, Hyun-Cheol High-resolution hyperspectral images of Council, Alaska 2019 |
topic_facet |
Remote sensing Hyperspectral UAV Permafrost Council, Alaska |
description |
Hyperspectral data are gaining popularity in remote sensing and signal processing communities because of the increased spectral information relative to multispectral data. Several airborne and spaceborne hyperspectral datasets are publicly available, facilitating the development of various applications and algorithms. However, hyperspectral data are usually limited by their narrow, highly correlated, and contiguous spectral bands in both processing and analysis. Moreover, the resolution of available hyperspectral datasets is not sufficiently high for the identification of small objects. Nevertheless, with the rapidly advancing technology, hyperspectral imaging systems can now be mounted on small aerial vehicles for detecting small objects at low altitude. To properly handle these high spectral and spatial resolution data, new or redesigned data processing or analysis pipelines must be developed. However, such datasets are currently not publicly available. Therefore, we provide two hyperspectral datasets from GNSS/INS-assisted co-aligned pushbroom hyperspectral scanners on a drone. The hyperspectral datasets consist of raw/post-processed hyperspectral data, raw/post-processed Global Navigation System Satellite/Inertial Measurement Unit (GNSS/IMU) data, and digital surface models, and were radiometrically and geometrically evaluated. These datasets are expected to aid the improvement of UAV-based hyperspectral data processing and analysis algorithms. |
format |
Dataset |
author |
Chi, Junhwa Kim, Jae-In Masjedi, Ali Flatt, John Evan Crawford, Melba M. Habib, Ayman F. Lee, Joohan Kim, Hyun-Cheol |
author_facet |
Chi, Junhwa Kim, Jae-In Masjedi, Ali Flatt, John Evan Crawford, Melba M. Habib, Ayman F. Lee, Joohan Kim, Hyun-Cheol |
author_sort |
Chi, Junhwa |
title |
High-resolution hyperspectral images of Council, Alaska 2019 |
title_short |
High-resolution hyperspectral images of Council, Alaska 2019 |
title_full |
High-resolution hyperspectral images of Council, Alaska 2019 |
title_fullStr |
High-resolution hyperspectral images of Council, Alaska 2019 |
title_full_unstemmed |
High-resolution hyperspectral images of Council, Alaska 2019 |
title_sort |
high-resolution hyperspectral images of council, alaska 2019 |
publisher |
NSF Arctic Data Center |
publishDate |
2021 |
url |
https://dx.doi.org/10.18739/a2416t10d https://arcticdata.io/catalog/view/doi:10.18739/A2416T10D |
genre |
permafrost Alaska |
genre_facet |
permafrost Alaska |
op_doi |
https://doi.org/10.18739/a2416t10d |
_version_ |
1766166331267219456 |